Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f8a4271c160>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f8a3f4be240>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32)
    
    return (input_real, input_z, learning_rate)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-bd0e58f58f59>", line 23, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/giancos/Dropbox/2017-06 - Udacity Deep Learning NanoDegree/dlnd-Project5/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/giancos/Dropbox/2017-06 - Udacity Deep Learning NanoDegree/dlnd-Project5/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/giancos/Dropbox/2017-06 - Udacity Deep Learning NanoDegree/dlnd-Project5/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/giancos/Dropbox/2017-06 - Udacity Deep Learning NanoDegree/dlnd-Project5/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/giancos/anaconda3/envs/dlnd-Project5/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    leaky_relu = lambda x: tf.maximum(0.01 * x, x)

    def conv(inputs, filters, strides):
        outputs = tf.layers.conv2d(inputs, filters, 5, strides, 'same')
        outputs = tf.layers.batch_normalization(outputs, training=True)
        outputs = leaky_relu(outputs)
        return outputs
    
    
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28xchannels with NO batch normalization
        # input 28*28*3
        x1 = conv(images, 64, 2) # 14*14*64
        x2 = conv(x1, 128, 2) # 7*7*128
        x3 = conv(x2, 256, 2) # 4*4*256  
        x4 = conv(x3, 512, 2) # 2*2*512  
        
        # Flatten it
        flat = tf.reshape(x3, (-1, 2*2*512))
        logits = tf.layers.dense(flat, 1)
        output = tf.sigmoid(logits)
        
        return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    leaky_relu = lambda x: tf.maximum(0.01 * x, x)
    
    
    def conv_transpose(inputs, filters, strides):        
        outputs = tf.layers.conv2d_transpose(inputs, filters, 5, strides, 'SAME')
        outputs = tf.layers.batch_normalization(outputs, training=is_train)
        outputs = leaky_relu(outputs)        
        return outputs
    
    
    with tf.variable_scope("generator", reuse=(not is_train)):
        
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = leaky_relu(x1)
             
        x2 = conv_transpose(x1, 256, 1)       
        x3 = conv_transpose(x2, 128, 2)          
        
    
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, 2, 'SAME')
        out = tf.tanh(logits)
        # 28*28*out_channel_dim

    
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    g_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake))
    g_loss = tf.reduce_mean(g_loss)
    
    
    d_loss_real = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * 0.9)
    d_loss_real = tf.reduce_mean(d_loss_real)
    
    d_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake))
    d_loss_fake = tf.reduce_mean(d_loss_fake)
    
    d_loss = d_loss_real + d_loss_fake
    

    return (d_loss, g_loss)


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    with tf.control_dependencies(update_ops):
        t_vars = tf.trainable_variables()
        
        d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
        g_vars = [var for var in t_vars if var.name.startswith('generator')]

        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

        return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                    
            show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 128
z_dim = 128
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 0.5994... Generator Loss: 6.2191
Epoch 1/2... Batch 20... Discriminator Loss: 0.6348... Generator Loss: 4.7256
Epoch 1/2... Batch 30... Discriminator Loss: 0.5713... Generator Loss: 2.0953
Epoch 1/2... Batch 40... Discriminator Loss: 0.5570... Generator Loss: 2.1437
Epoch 1/2... Batch 50... Discriminator Loss: 3.9303... Generator Loss: 7.2515
Epoch 1/2... Batch 60... Discriminator Loss: 0.9422... Generator Loss: 2.2153
Epoch 1/2... Batch 70... Discriminator Loss: 0.9066... Generator Loss: 2.1129
Epoch 1/2... Batch 80... Discriminator Loss: 1.4846... Generator Loss: 0.6544
Epoch 1/2... Batch 90... Discriminator Loss: 0.9627... Generator Loss: 1.2924
Epoch 1/2... Batch 100... Discriminator Loss: 1.0791... Generator Loss: 0.9494
Epoch 1/2... Batch 110... Discriminator Loss: 1.2697... Generator Loss: 1.6348
Epoch 1/2... Batch 120... Discriminator Loss: 1.2781... Generator Loss: 2.4449
Epoch 1/2... Batch 130... Discriminator Loss: 1.1400... Generator Loss: 0.8589
Epoch 1/2... Batch 140... Discriminator Loss: 1.4184... Generator Loss: 0.6251
Epoch 1/2... Batch 150... Discriminator Loss: 1.8229... Generator Loss: 0.3329
Epoch 1/2... Batch 160... Discriminator Loss: 1.1874... Generator Loss: 1.0426
Epoch 1/2... Batch 170... Discriminator Loss: 1.3599... Generator Loss: 0.6582
Epoch 1/2... Batch 180... Discriminator Loss: 1.2492... Generator Loss: 0.8329
Epoch 1/2... Batch 190... Discriminator Loss: 1.3730... Generator Loss: 0.8744
Epoch 1/2... Batch 200... Discriminator Loss: 1.3533... Generator Loss: 0.9368
Epoch 1/2... Batch 210... Discriminator Loss: 1.5636... Generator Loss: 1.7557
Epoch 1/2... Batch 220... Discriminator Loss: 1.2465... Generator Loss: 0.8182
Epoch 1/2... Batch 230... Discriminator Loss: 1.2655... Generator Loss: 1.2022
Epoch 1/2... Batch 240... Discriminator Loss: 1.3487... Generator Loss: 1.2279
Epoch 1/2... Batch 250... Discriminator Loss: 1.2352... Generator Loss: 0.7055
Epoch 1/2... Batch 260... Discriminator Loss: 1.1796... Generator Loss: 1.1908
Epoch 1/2... Batch 270... Discriminator Loss: 1.1969... Generator Loss: 0.8758
Epoch 1/2... Batch 280... Discriminator Loss: 1.1037... Generator Loss: 1.2792
Epoch 1/2... Batch 290... Discriminator Loss: 1.3011... Generator Loss: 1.2517
Epoch 1/2... Batch 300... Discriminator Loss: 1.2281... Generator Loss: 0.8129
Epoch 1/2... Batch 310... Discriminator Loss: 1.2293... Generator Loss: 1.2666
Epoch 1/2... Batch 320... Discriminator Loss: 1.5829... Generator Loss: 0.4147
Epoch 1/2... Batch 330... Discriminator Loss: 1.2057... Generator Loss: 0.9746
Epoch 1/2... Batch 340... Discriminator Loss: 1.1267... Generator Loss: 1.0194
Epoch 1/2... Batch 350... Discriminator Loss: 1.2052... Generator Loss: 0.9594
Epoch 1/2... Batch 360... Discriminator Loss: 1.4052... Generator Loss: 0.5564
Epoch 1/2... Batch 370... Discriminator Loss: 1.3224... Generator Loss: 0.6943
Epoch 1/2... Batch 380... Discriminator Loss: 1.1825... Generator Loss: 0.8374
Epoch 1/2... Batch 390... Discriminator Loss: 1.1100... Generator Loss: 1.0857
Epoch 1/2... Batch 400... Discriminator Loss: 1.4414... Generator Loss: 0.4606
Epoch 1/2... Batch 410... Discriminator Loss: 1.1719... Generator Loss: 1.0554
Epoch 1/2... Batch 420... Discriminator Loss: 1.1394... Generator Loss: 0.9638
Epoch 1/2... Batch 430... Discriminator Loss: 1.2048... Generator Loss: 1.4119
Epoch 1/2... Batch 440... Discriminator Loss: 1.1924... Generator Loss: 0.8350
Epoch 1/2... Batch 450... Discriminator Loss: 1.2812... Generator Loss: 1.7672
Epoch 1/2... Batch 460... Discriminator Loss: 1.1810... Generator Loss: 1.1600
Epoch 2/2... Batch 10... Discriminator Loss: 1.2079... Generator Loss: 0.7154
Epoch 2/2... Batch 20... Discriminator Loss: 1.8643... Generator Loss: 2.5531
Epoch 2/2... Batch 30... Discriminator Loss: 1.2240... Generator Loss: 0.7562
Epoch 2/2... Batch 40... Discriminator Loss: 1.1930... Generator Loss: 1.1012
Epoch 2/2... Batch 50... Discriminator Loss: 1.1274... Generator Loss: 1.1488
Epoch 2/2... Batch 60... Discriminator Loss: 1.1478... Generator Loss: 1.4301
Epoch 2/2... Batch 70... Discriminator Loss: 1.2094... Generator Loss: 1.1910
Epoch 2/2... Batch 80... Discriminator Loss: 1.1675... Generator Loss: 0.7915
Epoch 2/2... Batch 90... Discriminator Loss: 1.1208... Generator Loss: 1.1173
Epoch 2/2... Batch 100... Discriminator Loss: 1.3116... Generator Loss: 0.5749
Epoch 2/2... Batch 110... Discriminator Loss: 1.0738... Generator Loss: 1.5695
Epoch 2/2... Batch 120... Discriminator Loss: 1.2390... Generator Loss: 0.7199
Epoch 2/2... Batch 130... Discriminator Loss: 1.0608... Generator Loss: 1.3252
Epoch 2/2... Batch 140... Discriminator Loss: 1.1635... Generator Loss: 0.9311
Epoch 2/2... Batch 150... Discriminator Loss: 1.0846... Generator Loss: 1.0056
Epoch 2/2... Batch 160... Discriminator Loss: 1.2749... Generator Loss: 1.7788
Epoch 2/2... Batch 170... Discriminator Loss: 1.1306... Generator Loss: 1.1986
Epoch 2/2... Batch 180... Discriminator Loss: 1.2469... Generator Loss: 1.3022
Epoch 2/2... Batch 190... Discriminator Loss: 1.5612... Generator Loss: 1.8625
Epoch 2/2... Batch 200... Discriminator Loss: 1.1550... Generator Loss: 0.9351
Epoch 2/2... Batch 210... Discriminator Loss: 1.3110... Generator Loss: 0.6131
Epoch 2/2... Batch 220... Discriminator Loss: 1.1061... Generator Loss: 1.3173
Epoch 2/2... Batch 230... Discriminator Loss: 1.0985... Generator Loss: 0.8465
Epoch 2/2... Batch 240... Discriminator Loss: 1.2402... Generator Loss: 0.9927
Epoch 2/2... Batch 250... Discriminator Loss: 1.2196... Generator Loss: 0.9612
Epoch 2/2... Batch 260... Discriminator Loss: 1.2366... Generator Loss: 1.8342
Epoch 2/2... Batch 270... Discriminator Loss: 1.3497... Generator Loss: 0.5552
Epoch 2/2... Batch 280... Discriminator Loss: 1.1800... Generator Loss: 0.8794
Epoch 2/2... Batch 290... Discriminator Loss: 1.2425... Generator Loss: 0.9265
Epoch 2/2... Batch 300... Discriminator Loss: 1.0884... Generator Loss: 1.7503
Epoch 2/2... Batch 310... Discriminator Loss: 1.1721... Generator Loss: 0.8095
Epoch 2/2... Batch 320... Discriminator Loss: 1.2825... Generator Loss: 1.6913
Epoch 2/2... Batch 330... Discriminator Loss: 1.4838... Generator Loss: 0.6345
Epoch 2/2... Batch 340... Discriminator Loss: 1.0488... Generator Loss: 1.4072
Epoch 2/2... Batch 350... Discriminator Loss: 1.2332... Generator Loss: 0.7796
Epoch 2/2... Batch 360... Discriminator Loss: 1.0182... Generator Loss: 1.2177
Epoch 2/2... Batch 370... Discriminator Loss: 0.9540... Generator Loss: 1.5254
Epoch 2/2... Batch 380... Discriminator Loss: 1.0409... Generator Loss: 1.0340
Epoch 2/2... Batch 390... Discriminator Loss: 0.9737... Generator Loss: 1.0506
Epoch 2/2... Batch 400... Discriminator Loss: 1.0555... Generator Loss: 1.0992
Epoch 2/2... Batch 410... Discriminator Loss: 1.0415... Generator Loss: 1.3609
Epoch 2/2... Batch 420... Discriminator Loss: 1.4356... Generator Loss: 0.5105
Epoch 2/2... Batch 430... Discriminator Loss: 0.9238... Generator Loss: 1.3144
Epoch 2/2... Batch 440... Discriminator Loss: 1.3894... Generator Loss: 0.5483
Epoch 2/2... Batch 450... Discriminator Loss: 1.1601... Generator Loss: 1.1726
Epoch 2/2... Batch 460... Discriminator Loss: 1.1128... Generator Loss: 0.8536

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.25


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 2.4922... Generator Loss: 0.1539
Epoch 1/1... Batch 20... Discriminator Loss: 1.1165... Generator Loss: 0.8674
Epoch 1/1... Batch 30... Discriminator Loss: 1.3315... Generator Loss: 0.5588
Epoch 1/1... Batch 40... Discriminator Loss: 0.9087... Generator Loss: 1.0537
Epoch 1/1... Batch 50... Discriminator Loss: 0.8880... Generator Loss: 2.1264
Epoch 1/1... Batch 60... Discriminator Loss: 0.5921... Generator Loss: 2.5175
Epoch 1/1... Batch 70... Discriminator Loss: 0.8756... Generator Loss: 1.1441
Epoch 1/1... Batch 80... Discriminator Loss: 0.9044... Generator Loss: 1.7931
Epoch 1/1... Batch 90... Discriminator Loss: 0.8175... Generator Loss: 1.6486
Epoch 1/1... Batch 100... Discriminator Loss: 0.6672... Generator Loss: 2.1264
Epoch 1/1... Batch 110... Discriminator Loss: 0.5770... Generator Loss: 2.4198
Epoch 1/1... Batch 120... Discriminator Loss: 0.7825... Generator Loss: 1.3904
Epoch 1/1... Batch 130... Discriminator Loss: 0.7708... Generator Loss: 1.3691
Epoch 1/1... Batch 140... Discriminator Loss: 0.6258... Generator Loss: 1.6725
Epoch 1/1... Batch 150... Discriminator Loss: 0.6769... Generator Loss: 1.5876
Epoch 1/1... Batch 160... Discriminator Loss: 0.6737... Generator Loss: 1.5179
Epoch 1/1... Batch 170... Discriminator Loss: 1.3546... Generator Loss: 0.5410
Epoch 1/1... Batch 180... Discriminator Loss: 0.9138... Generator Loss: 1.2258
Epoch 1/1... Batch 190... Discriminator Loss: 0.9191... Generator Loss: 1.5049
Epoch 1/1... Batch 200... Discriminator Loss: 1.6937... Generator Loss: 0.3735
Epoch 1/1... Batch 210... Discriminator Loss: 1.1728... Generator Loss: 0.7668
Epoch 1/1... Batch 220... Discriminator Loss: 1.2398... Generator Loss: 0.7116
Epoch 1/1... Batch 230... Discriminator Loss: 1.1507... Generator Loss: 0.7539
Epoch 1/1... Batch 240... Discriminator Loss: 1.1157... Generator Loss: 0.8879
Epoch 1/1... Batch 250... Discriminator Loss: 1.1941... Generator Loss: 0.9253
Epoch 1/1... Batch 260... Discriminator Loss: 1.0538... Generator Loss: 1.7841
Epoch 1/1... Batch 270... Discriminator Loss: 1.0437... Generator Loss: 1.2962
Epoch 1/1... Batch 280... Discriminator Loss: 0.8845... Generator Loss: 1.1670
Epoch 1/1... Batch 290... Discriminator Loss: 1.0253... Generator Loss: 1.0386
Epoch 1/1... Batch 300... Discriminator Loss: 1.0759... Generator Loss: 0.7890
Epoch 1/1... Batch 310... Discriminator Loss: 0.8589... Generator Loss: 1.0676
Epoch 1/1... Batch 320... Discriminator Loss: 1.0058... Generator Loss: 1.0771
Epoch 1/1... Batch 330... Discriminator Loss: 0.8796... Generator Loss: 1.0478
Epoch 1/1... Batch 340... Discriminator Loss: 1.0882... Generator Loss: 2.1253
Epoch 1/1... Batch 350... Discriminator Loss: 0.7406... Generator Loss: 1.7144
Epoch 1/1... Batch 360... Discriminator Loss: 0.8870... Generator Loss: 2.2050
Epoch 1/1... Batch 370... Discriminator Loss: 0.9223... Generator Loss: 2.5187
Epoch 1/1... Batch 380... Discriminator Loss: 0.9564... Generator Loss: 1.3458
Epoch 1/1... Batch 390... Discriminator Loss: 0.8843... Generator Loss: 1.3560
Epoch 1/1... Batch 400... Discriminator Loss: 1.3622... Generator Loss: 0.5483
Epoch 1/1... Batch 410... Discriminator Loss: 0.9865... Generator Loss: 0.9351
Epoch 1/1... Batch 420... Discriminator Loss: 1.0028... Generator Loss: 1.2958
Epoch 1/1... Batch 430... Discriminator Loss: 0.8852... Generator Loss: 1.1583
Epoch 1/1... Batch 440... Discriminator Loss: 1.3403... Generator Loss: 0.5772
Epoch 1/1... Batch 450... Discriminator Loss: 0.9818... Generator Loss: 1.1160
Epoch 1/1... Batch 460... Discriminator Loss: 0.7599... Generator Loss: 1.8118
Epoch 1/1... Batch 470... Discriminator Loss: 0.8362... Generator Loss: 1.2474
Epoch 1/1... Batch 480... Discriminator Loss: 0.9170... Generator Loss: 1.2429
Epoch 1/1... Batch 490... Discriminator Loss: 1.0104... Generator Loss: 0.9604
Epoch 1/1... Batch 500... Discriminator Loss: 0.8434... Generator Loss: 1.5838
Epoch 1/1... Batch 510... Discriminator Loss: 1.3406... Generator Loss: 0.5371
Epoch 1/1... Batch 520... Discriminator Loss: 1.0402... Generator Loss: 0.9922
Epoch 1/1... Batch 530... Discriminator Loss: 1.0406... Generator Loss: 1.0246
Epoch 1/1... Batch 540... Discriminator Loss: 1.0299... Generator Loss: 1.4834
Epoch 1/1... Batch 550... Discriminator Loss: 1.3073... Generator Loss: 0.5853
Epoch 1/1... Batch 560... Discriminator Loss: 1.0744... Generator Loss: 1.0034
Epoch 1/1... Batch 570... Discriminator Loss: 1.0286... Generator Loss: 0.9423
Epoch 1/1... Batch 580... Discriminator Loss: 1.0640... Generator Loss: 0.8374
Epoch 1/1... Batch 590... Discriminator Loss: 1.0424... Generator Loss: 0.7962
Epoch 1/1... Batch 600... Discriminator Loss: 1.0863... Generator Loss: 0.8643
Epoch 1/1... Batch 610... Discriminator Loss: 1.6059... Generator Loss: 0.3923
Epoch 1/1... Batch 620... Discriminator Loss: 1.0568... Generator Loss: 1.6870
Epoch 1/1... Batch 630... Discriminator Loss: 1.3332... Generator Loss: 0.5779
Epoch 1/1... Batch 640... Discriminator Loss: 1.0696... Generator Loss: 1.3905
Epoch 1/1... Batch 650... Discriminator Loss: 0.9029... Generator Loss: 1.3542
Epoch 1/1... Batch 660... Discriminator Loss: 1.1017... Generator Loss: 1.5850
Epoch 1/1... Batch 670... Discriminator Loss: 1.0893... Generator Loss: 1.0556
Epoch 1/1... Batch 680... Discriminator Loss: 1.0874... Generator Loss: 1.1376
Epoch 1/1... Batch 690... Discriminator Loss: 1.3266... Generator Loss: 1.6816
Epoch 1/1... Batch 700... Discriminator Loss: 1.4705... Generator Loss: 0.4672
Epoch 1/1... Batch 710... Discriminator Loss: 1.0379... Generator Loss: 0.9306
Epoch 1/1... Batch 720... Discriminator Loss: 1.1722... Generator Loss: 0.7102
Epoch 1/1... Batch 730... Discriminator Loss: 1.3087... Generator Loss: 0.5776
Epoch 1/1... Batch 740... Discriminator Loss: 1.4539... Generator Loss: 0.4637
Epoch 1/1... Batch 750... Discriminator Loss: 1.1401... Generator Loss: 1.0851
Epoch 1/1... Batch 760... Discriminator Loss: 0.9400... Generator Loss: 1.3379
Epoch 1/1... Batch 770... Discriminator Loss: 1.1869... Generator Loss: 1.5912
Epoch 1/1... Batch 780... Discriminator Loss: 1.1527... Generator Loss: 0.7607
Epoch 1/1... Batch 790... Discriminator Loss: 1.2264... Generator Loss: 1.4818
Epoch 1/1... Batch 800... Discriminator Loss: 1.2590... Generator Loss: 0.6054
Epoch 1/1... Batch 810... Discriminator Loss: 1.2624... Generator Loss: 0.6279
Epoch 1/1... Batch 820... Discriminator Loss: 1.1857... Generator Loss: 0.7028
Epoch 1/1... Batch 830... Discriminator Loss: 1.4364... Generator Loss: 0.4754
Epoch 1/1... Batch 840... Discriminator Loss: 0.9294... Generator Loss: 1.5108
Epoch 1/1... Batch 850... Discriminator Loss: 0.9812... Generator Loss: 0.9603
Epoch 1/1... Batch 860... Discriminator Loss: 1.4881... Generator Loss: 1.6863
Epoch 1/1... Batch 870... Discriminator Loss: 1.0532... Generator Loss: 1.3707
Epoch 1/1... Batch 880... Discriminator Loss: 1.4028... Generator Loss: 0.4738
Epoch 1/1... Batch 890... Discriminator Loss: 1.0668... Generator Loss: 0.8177
Epoch 1/1... Batch 900... Discriminator Loss: 1.0106... Generator Loss: 1.4349
Epoch 1/1... Batch 910... Discriminator Loss: 1.1292... Generator Loss: 0.7815
Epoch 1/1... Batch 920... Discriminator Loss: 1.3306... Generator Loss: 0.5307
Epoch 1/1... Batch 930... Discriminator Loss: 1.1886... Generator Loss: 1.2202
Epoch 1/1... Batch 940... Discriminator Loss: 1.1437... Generator Loss: 0.6978
Epoch 1/1... Batch 950... Discriminator Loss: 1.3392... Generator Loss: 0.5889
Epoch 1/1... Batch 960... Discriminator Loss: 1.0021... Generator Loss: 1.0646
Epoch 1/1... Batch 970... Discriminator Loss: 1.2039... Generator Loss: 0.6400
Epoch 1/1... Batch 980... Discriminator Loss: 1.3017... Generator Loss: 0.5684
Epoch 1/1... Batch 990... Discriminator Loss: 1.7737... Generator Loss: 0.3080
Epoch 1/1... Batch 1000... Discriminator Loss: 1.1248... Generator Loss: 0.7019
Epoch 1/1... Batch 1010... Discriminator Loss: 0.8909... Generator Loss: 1.0722
Epoch 1/1... Batch 1020... Discriminator Loss: 1.2450... Generator Loss: 0.6358
Epoch 1/1... Batch 1030... Discriminator Loss: 1.2640... Generator Loss: 0.7163
Epoch 1/1... Batch 1040... Discriminator Loss: 1.4946... Generator Loss: 0.4557
Epoch 1/1... Batch 1050... Discriminator Loss: 1.1040... Generator Loss: 1.1161
Epoch 1/1... Batch 1060... Discriminator Loss: 1.5120... Generator Loss: 0.4341
Epoch 1/1... Batch 1070... Discriminator Loss: 1.2155... Generator Loss: 0.9958
Epoch 1/1... Batch 1080... Discriminator Loss: 1.1583... Generator Loss: 1.0314
Epoch 1/1... Batch 1090... Discriminator Loss: 1.4766... Generator Loss: 1.6913
Epoch 1/1... Batch 1100... Discriminator Loss: 1.1739... Generator Loss: 1.1449
Epoch 1/1... Batch 1110... Discriminator Loss: 1.4069... Generator Loss: 1.7063
Epoch 1/1... Batch 1120... Discriminator Loss: 0.9562... Generator Loss: 1.0104
Epoch 1/1... Batch 1130... Discriminator Loss: 1.1908... Generator Loss: 0.6565
Epoch 1/1... Batch 1140... Discriminator Loss: 1.2279... Generator Loss: 1.4056
Epoch 1/1... Batch 1150... Discriminator Loss: 1.1471... Generator Loss: 0.8458
Epoch 1/1... Batch 1160... Discriminator Loss: 1.2089... Generator Loss: 0.7420
Epoch 1/1... Batch 1170... Discriminator Loss: 0.9584... Generator Loss: 1.2545
Epoch 1/1... Batch 1180... Discriminator Loss: 1.4851... Generator Loss: 0.4397
Epoch 1/1... Batch 1190... Discriminator Loss: 1.2950... Generator Loss: 0.5604
Epoch 1/1... Batch 1200... Discriminator Loss: 1.0205... Generator Loss: 0.9444
Epoch 1/1... Batch 1210... Discriminator Loss: 1.1218... Generator Loss: 1.3467
Epoch 1/1... Batch 1220... Discriminator Loss: 0.8810... Generator Loss: 1.2228
Epoch 1/1... Batch 1230... Discriminator Loss: 0.9831... Generator Loss: 1.0621
Epoch 1/1... Batch 1240... Discriminator Loss: 0.9658... Generator Loss: 1.0391
Epoch 1/1... Batch 1250... Discriminator Loss: 1.1870... Generator Loss: 0.8745
Epoch 1/1... Batch 1260... Discriminator Loss: 1.5716... Generator Loss: 0.4169
Epoch 1/1... Batch 1270... Discriminator Loss: 0.9355... Generator Loss: 1.3482
Epoch 1/1... Batch 1280... Discriminator Loss: 1.1656... Generator Loss: 0.7441
Epoch 1/1... Batch 1290... Discriminator Loss: 1.8729... Generator Loss: 1.7225
Epoch 1/1... Batch 1300... Discriminator Loss: 0.9459... Generator Loss: 1.2423
Epoch 1/1... Batch 1310... Discriminator Loss: 1.1363... Generator Loss: 0.7871
Epoch 1/1... Batch 1320... Discriminator Loss: 0.8778... Generator Loss: 1.4326
Epoch 1/1... Batch 1330... Discriminator Loss: 1.3647... Generator Loss: 0.5717
Epoch 1/1... Batch 1340... Discriminator Loss: 1.1593... Generator Loss: 0.6807
Epoch 1/1... Batch 1350... Discriminator Loss: 1.0805... Generator Loss: 0.7973
Epoch 1/1... Batch 1360... Discriminator Loss: 1.1730... Generator Loss: 1.7106
Epoch 1/1... Batch 1370... Discriminator Loss: 1.6081... Generator Loss: 0.3737
Epoch 1/1... Batch 1380... Discriminator Loss: 1.1538... Generator Loss: 1.0827
Epoch 1/1... Batch 1390... Discriminator Loss: 1.4588... Generator Loss: 0.5093
Epoch 1/1... Batch 1400... Discriminator Loss: 1.2937... Generator Loss: 0.6855
Epoch 1/1... Batch 1410... Discriminator Loss: 1.2439... Generator Loss: 0.8844
Epoch 1/1... Batch 1420... Discriminator Loss: 1.0606... Generator Loss: 0.9456
Epoch 1/1... Batch 1430... Discriminator Loss: 1.2287... Generator Loss: 0.7759
Epoch 1/1... Batch 1440... Discriminator Loss: 1.3240... Generator Loss: 0.5623
Epoch 1/1... Batch 1450... Discriminator Loss: 1.2520... Generator Loss: 0.7666
Epoch 1/1... Batch 1460... Discriminator Loss: 1.2035... Generator Loss: 0.8156
Epoch 1/1... Batch 1470... Discriminator Loss: 1.3053... Generator Loss: 0.5826
Epoch 1/1... Batch 1480... Discriminator Loss: 1.1143... Generator Loss: 0.9935
Epoch 1/1... Batch 1490... Discriminator Loss: 1.1612... Generator Loss: 1.1130
Epoch 1/1... Batch 1500... Discriminator Loss: 1.0422... Generator Loss: 0.9576
Epoch 1/1... Batch 1510... Discriminator Loss: 1.3578... Generator Loss: 0.6346
Epoch 1/1... Batch 1520... Discriminator Loss: 1.2452... Generator Loss: 0.6978
Epoch 1/1... Batch 1530... Discriminator Loss: 1.1777... Generator Loss: 0.9196
Epoch 1/1... Batch 1540... Discriminator Loss: 1.1548... Generator Loss: 1.3234
Epoch 1/1... Batch 1550... Discriminator Loss: 1.1385... Generator Loss: 1.0583
Epoch 1/1... Batch 1560... Discriminator Loss: 1.4721... Generator Loss: 0.4760
Epoch 1/1... Batch 1570... Discriminator Loss: 1.3359... Generator Loss: 0.6682
Epoch 1/1... Batch 1580... Discriminator Loss: 1.2937... Generator Loss: 0.8564
Epoch 1/1... Batch 1590... Discriminator Loss: 1.2087... Generator Loss: 0.8917
Epoch 1/1... Batch 1600... Discriminator Loss: 1.2588... Generator Loss: 0.7489
Epoch 1/1... Batch 1610... Discriminator Loss: 1.1673... Generator Loss: 0.7562
Epoch 1/1... Batch 1620... Discriminator Loss: 1.3654... Generator Loss: 0.6126
Epoch 1/1... Batch 1630... Discriminator Loss: 1.2902... Generator Loss: 0.7266
Epoch 1/1... Batch 1640... Discriminator Loss: 1.0887... Generator Loss: 0.8602
Epoch 1/1... Batch 1650... Discriminator Loss: 1.2478... Generator Loss: 0.7449
Epoch 1/1... Batch 1660... Discriminator Loss: 1.1904... Generator Loss: 0.8759
Epoch 1/1... Batch 1670... Discriminator Loss: 1.2399... Generator Loss: 0.7576
Epoch 1/1... Batch 1680... Discriminator Loss: 1.2186... Generator Loss: 0.6971
Epoch 1/1... Batch 1690... Discriminator Loss: 1.2972... Generator Loss: 0.6597
Epoch 1/1... Batch 1700... Discriminator Loss: 1.3340... Generator Loss: 0.6529
Epoch 1/1... Batch 1710... Discriminator Loss: 1.1140... Generator Loss: 0.9448
Epoch 1/1... Batch 1720... Discriminator Loss: 1.1451... Generator Loss: 0.8907
Epoch 1/1... Batch 1730... Discriminator Loss: 1.3628... Generator Loss: 0.6864
Epoch 1/1... Batch 1740... Discriminator Loss: 1.2659... Generator Loss: 0.7111
Epoch 1/1... Batch 1750... Discriminator Loss: 1.3723... Generator Loss: 0.7209
Epoch 1/1... Batch 1760... Discriminator Loss: 1.3291... Generator Loss: 0.6647
Epoch 1/1... Batch 1770... Discriminator Loss: 1.2632... Generator Loss: 0.8506
Epoch 1/1... Batch 1780... Discriminator Loss: 1.2504... Generator Loss: 0.7003
Epoch 1/1... Batch 1790... Discriminator Loss: 1.3168... Generator Loss: 0.7628
Epoch 1/1... Batch 1800... Discriminator Loss: 1.2521... Generator Loss: 0.7873
Epoch 1/1... Batch 1810... Discriminator Loss: 1.2283... Generator Loss: 0.6883
Epoch 1/1... Batch 1820... Discriminator Loss: 1.2695... Generator Loss: 0.6655
Epoch 1/1... Batch 1830... Discriminator Loss: 1.2528... Generator Loss: 0.8740
Epoch 1/1... Batch 1840... Discriminator Loss: 1.4199... Generator Loss: 0.5444
Epoch 1/1... Batch 1850... Discriminator Loss: 1.2635... Generator Loss: 0.7337
Epoch 1/1... Batch 1860... Discriminator Loss: 1.2255... Generator Loss: 0.7052
Epoch 1/1... Batch 1870... Discriminator Loss: 1.2201... Generator Loss: 0.8201
Epoch 1/1... Batch 1880... Discriminator Loss: 1.1443... Generator Loss: 0.9237
Epoch 1/1... Batch 1890... Discriminator Loss: 1.2672... Generator Loss: 0.6437
Epoch 1/1... Batch 1900... Discriminator Loss: 1.4991... Generator Loss: 0.4643
Epoch 1/1... Batch 1910... Discriminator Loss: 1.4939... Generator Loss: 0.4940
Epoch 1/1... Batch 1920... Discriminator Loss: 1.5986... Generator Loss: 0.4633
Epoch 1/1... Batch 1930... Discriminator Loss: 1.2235... Generator Loss: 0.7143
Epoch 1/1... Batch 1940... Discriminator Loss: 1.0897... Generator Loss: 0.9094
Epoch 1/1... Batch 1950... Discriminator Loss: 1.2998... Generator Loss: 0.7650
Epoch 1/1... Batch 1960... Discriminator Loss: 1.3461... Generator Loss: 0.5442
Epoch 1/1... Batch 1970... Discriminator Loss: 1.5491... Generator Loss: 0.4234
Epoch 1/1... Batch 1980... Discriminator Loss: 1.2320... Generator Loss: 0.8475
Epoch 1/1... Batch 1990... Discriminator Loss: 1.3798... Generator Loss: 0.6320
Epoch 1/1... Batch 2000... Discriminator Loss: 1.3979... Generator Loss: 0.5519
Epoch 1/1... Batch 2010... Discriminator Loss: 1.2584... Generator Loss: 0.7484
Epoch 1/1... Batch 2020... Discriminator Loss: 1.4617... Generator Loss: 0.5500
Epoch 1/1... Batch 2030... Discriminator Loss: 1.4178... Generator Loss: 0.5111
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Epoch 1/1... Batch 5860... Discriminator Loss: 1.2328... Generator Loss: 1.0115
Epoch 1/1... Batch 5870... Discriminator Loss: 1.1836... Generator Loss: 0.7583
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Epoch 1/1... Batch 5890... Discriminator Loss: 1.5746... Generator Loss: 0.4146
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Epoch 1/1... Batch 6210... Discriminator Loss: 1.5750... Generator Loss: 0.4331
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Epoch 1/1... Batch 6310... Discriminator Loss: 1.4871... Generator Loss: 0.4775
Epoch 1/1... Batch 6320... Discriminator Loss: 1.2809... Generator Loss: 0.8852
Epoch 1/1... Batch 6330... Discriminator Loss: 1.5515... Generator Loss: 0.5113

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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